Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma
Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm
Validation Study of Supervised and Unsupervised Calcification-Algorithms Used to Detection of Melanoma, International Journal of Biomedical Engineering and Clinical Science.
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2015, pp. 1-9.
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